Diagnosis of Major Depressive Disorder Based on Individualized Brain Functional and Structural Connectivity.

Abstract

Background: Traditional neuroimaging studies have primarily emphasized analysis at the group level, often neglecting the specificity at the individual level. Recently, there has been a growing interest in individual differences in brain connectivity. Investigating individual-specific connectivity is important for understanding the mechanisms of major depressive disorder (MDD) and the variations among individuals.

Purpose: To integrate individualized functional connectivity and structural connectivity with machine learning techniques to distinguish people with MDD and healthy controls (HCs).

Study type: Prospective.

Subjects: A total of 182 patients with MDD and 157 HCs and a verification cohort including 54 patients and 46 HCs.

Field strength/sequence: 3.0 T/T1-weighted imaging, resting-state functional MRI with echo-planar sequence, and diffusion tensor imaging with single-shot spin echo.

Assessment: Functional and structural brain networks from rs-fMRI and DTI data were constructed, respectively. Based on these networks, individualized functional connectivity (IFC) and individualized structural connectivity (ISC) were extracted using common orthogonal basis extraction (COBE). Subsequently, multimodal canonical correlation analysis combined with joint independent component analysis (mCCA + jICA) was conducted to fusion analysis to identify the joint and unique independent components (ICs) across multiple modes. These ICs were utilized to generate features, and a support vector machine (SVM) model was implemented for the classification of MDD.

Statistical tests: The differences in individualized connectivity between patients and controls were compared using two-sample t test, with a significance threshold set at P < 0.05. The established model was tested and evaluated using the receiver operating characteristic (ROC) curve.

Results: The classification performance of the constructed individualized connectivity feature model after multisequence fusion increased from 72.2% to 90.3%. Furthermore, the prediction model showed significant predictive power for assessing the severity of depression in patients with MDD (r = 0.544).

Data conclusion: The integration of IFC and ISC through multisequence fusion enhances our capacity to identify MDD, highlighting the advantages of the individualized approach and underscoring its significance in MDD research.

Level of evidence: 1 TECHNICAL EFFICACY: Stage 2.